WCF-WebHttp and custom JSON error messages

I’m currently working on a solution that exposes a BizTalk Orchestration as a RESTful webservice using WCF-WebHttp. Upon successful processing of the message, the service returns binary data for the client to download. However, if the processing fails, I wanted the service to return application/json data with an error message that would be suitable for […]
Blog Post by: Dan Field

BizTalk Innovation Day, Oslo – story of the day

This is a guest blog post by Sandro Pereira, one of the speakers of the event.  Sandro Pereira works as a BizTalk Consultant at Devscope. Over the last few years, Sandro has been working on integration scenarios and Cloud Provisioning implementations at a major telecommunications service provider in Portugal. Sandro has been awarded the Microsoft […]

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Blog Post by: Saravana Kumar

BizTalk Mapping Patterns and Best Practices book [Free] released

BizTalk Mapping Patterns and Best Practices book [Free] released

I public announced for the first time that I was working in an eBook about BizTalk Mapping Patterns and Best Practices, and that it would be published for free, this March during BizTalk Summit 2014 London Well folks, it’s that time to announce that “BizTalk Mapping Patterns and Best Practices” book – a 400-page recipe […]
Blog Post by: Sandro Pereira

Aligning Microsoft Azure BizTalk Services Development with Enterprise Integration Patterns

We have just finished a fairly large effort in moving the QuickLearn TrainingTeam blog over to http://www.quicklearn.com/blog, as a result we had a mix-up with the link for our last post. This post has moved here: Aligning Microsoft Azure BizTalk … Continue reading →

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Blog Post by: Nick Hauenstein

How to upload an R package to Azure Machine Learning

How to upload an R package to Azure Machine Learning

Azure Machine Learning (http://azure.com/ml) has a number of packages already installed by default. You can see them with this following sample experiment:


R script is:

data.set <-data.frame(installed.packages());
# Select data.frame to be sent to the output Dataset port

you’ll find a little more than 400 packages.


Still you may need to use a package which is not known by Azure ML. Here is how to upload it to the environment.

NB: This post takes skmeans (k-means with a cosine distance) as an example, but this works for other packages as well.


Let’s suppose you have this code in R Studio locally.

NB: you can find information on how to setup your environment in this post. It’s in French, but bing translator is your friend.


sample_data <- matrix(sample.int(1000, size = 20*500, replace = TRUE), nrow = 20, ncol = 500, 
                      dimnames=list(1:20, 1:500))

fit <- skmeans(sample_data,5)

result <- data.frame(list(rownames(sample_data), fit$cluster), row.names=NULL)
colnames(result) <- c("sample data row", "cluster")


this will give this kind of result


If you try this in Azure ML, you’ll get the following result:



Here is how to have the script loading all the necessary packages in the Azure ML environment.



So let’s now see how you construct the skmeans_packages.zip and know which lines to write here:



On the local environment (in my case Windows), I remove the R packages that are installed in My Documents\R


then in R, I install the skmeans package:


this gives the following result:


So I know I have to install the following packages in order:

  • slam
  • clue
  • skmeans

Then I go to the temp folder:


I Zip the zips:


and rename this new zip file as skmeans_packages.zip


I then can upload it Azure ML:


image image


Then you’ll be able to find it as a saved dataset in your workspace:


After it has been connected to the third dot of the Execute R Script module instance, you’ll be able to find the content in src/ folder:


so, in order  to install skmeans and its two dependencies, then reference the skmeans library, you just have to enter the following lines:

install.packages("src/slam_0.1-32.zip", lib = ".", repos = NULL, verbose = TRUE)
install.packages("src/clue_0.3-48.zip", lib = ".", repos = NULL, verbose = TRUE)
install.packages("src/skmeans_0.2-6.zip", lib = ".", repos = NULL, verbose = TRUE)
library(skmeans, lib.loc=".", verbose=TRUE)


Azure ML has a pool of VM with docker-like containers (true Windows containers, named drawbridge) where the experiments run. So each time the script runs, it starts from a blank standard Azure ML environment. By bringing a zip, you add the files to that environment.


Hope this blog post will help you if you need R packages which are not in the 400+ preloaded ones in Azure Machine Learning!



Benjamin (@benjguin)

Blog Post by: Benjamin GUINEBERTIERE